{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87c9e826-1af1-4dbe-8798-8a7073c3494c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c6934ee-77ae-44d7-a7f8-ca6d6f9ce48c",
   "metadata": {},
   "source": [
    "## FacetGrid对象的基本用法\n",
    "\n",
    "FacetGrid提供了Figure级的接口，方便用户更为简单地调用各种Axes级别的绘图工具。\n",
    "\n",
    "#### 重点\n",
    "* FacetGrid对象可以调用map方法进行统一绘图\n",
    "    * map方法调用Axes级的方法进行绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa1cbaa2-3654-4314-831f-97d304630a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据(seaborn提供测试数据)\n",
    "tips = pd.read_csv(\"dataset/tips.csv\")\n",
    "# 查看数据\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5cbc590-235b-4483-9c74-d9117ac0bb6c",
   "metadata": {},
   "source": [
    "### FacetGrid对象绘制单张图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "805c463f-450f-4c9b-843c-538dfc39969f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nathanwu/Opt/mambaforge/envs/jpt/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18f8ac390>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 初始化一个FacetGrid对象\n",
    "fg1 = sns.FacetGrid(tips)\n",
    "# 调用FacetGrid的map绘图\n",
    "    # 第一个参数为需要调用Axes绘图（可以直接调用matplotlib的Axes），注意只能调用Axes级别\n",
    "    # 第二组参数为x和y轴对应的数据字段名，seaborn会把紧接的第一个字段视为x轴\n",
    "fg1.map(plt.scatter, \"total_bill\", \"tip\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859202a1-6800-4378-8fb9-ea28b6a76534",
   "metadata": {},
   "source": [
    "### FacetGrid对象绘制多张图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "beb7ebdc-144d-4a13-a472-c267f2660211",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nathanwu/Opt/mambaforge/envs/jpt/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18f89fb50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 初始化一个FacetGrid对象\n",
    "# col参数用于设置按指定的字段取值分列绘制子图\n",
    "    # 例如，col=\"day\"为设定按 “day”的取值分列绘图\n",
    "# row参数用于设置按指定的字段取值分行绘制子图\n",
    "    # 例如，row=\"time\"为设定按 “time”的取值分行绘图\n",
    "fg2 = sns.FacetGrid(tips, col=\"day\", row=\"time\")\n",
    "fg2.map(plt.scatter, \"total_bill\", \"tip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3bf47f1e-0697-4c02-af0c-57ca2bb4a8cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nathanwu/Opt/mambaforge/envs/jpt/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18fbcd5d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 初始化一个FacetGrid对象\n",
    "# hue参数用于设置按指定的字段取值分颜色绘制\n",
    "    # 例如，hue=\"time\"为设定按 “time”的取值在子图上绘图不同颜色\n",
    "fg3 = sns.FacetGrid(tips, col=\"day\", hue=\"time\")\n",
    "fg3.map(plt.scatter, \"total_bill\", \"tip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6da10a33-0569-45e6-a417-53dc5d40e5c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nathanwu/Opt/mambaforge/envs/jpt/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18fdb16d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 初始化一个FacetGrid对象\n",
    "# height参数用于设置所有子图的高度\n",
    "# aspect参数用于设置所有子图的宽高比（n:1）\n",
    "    # 例如，aspect=2 为设定按宽高比为 2:1\n",
    "fg4 = sns.FacetGrid(tips, col=\"smoker\", height=3, aspect=2)\n",
    "fg4.map(sns.barplot, \"day\", \"total_bill\", order=[\"Thur\", \"Fri\", \"Sat\", \"Sun\"])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
